In essence, all current popular detection methods are using the powerful data fitting capability of neural networks
to extract discriminative features for face forgery detection.
And detection methods based on deep learning usually pose face forgery detection as a binary classification problem
and use softmax lossFollowing [1]}, we define the softmax loss as the combination of the
last fully connected layer, softmax function, and cross-entropy loss. to supervise the training of CNN networks.
However, learned features supervised by softmax loss are not discriminative enough,
since softmax loss does not explicitly encourage intra-class compactness and inter-class separability, as illustrated in the left of Figure REF .
Recent work [2]} has noticed this problem and attempted to utilize triplet loss [3]} to extract discriminative features.
However, regular metric learning methods usually indiscriminately encourage the intra-class compactness of natural and manipulated faces in the embedding space.
Additionally, feature distributions of manipulated faces vary from one manipulation method to another considering various GAN fingerprints [4]} and some unique operations, as shown in the left of Figure REF ,
making it nontrivial to aggregate all the manipulated faces. Therefore, constraining intra-class compactness of samples generated by varied manipulation methods
usually leads to a sub-optimal solution because of optimization difficulty and even damages the performance owing to overfitting.
